Leveraging Terminological Structure for Object Reconciliation

  • Jan Noessner
  • Mathias Niepert
  • Christian Meilicke
  • Heiner Stuckenschmidt
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6089)


It has been argued that linked open data is the major benefit of semantic technologies for the web as it provides a huge amount of structured data that can be accessed in a more effective way than web pages. While linked open data avoids many problems connected with the use of expressive ontologies such as the knowledge acquisition bottleneck, data heterogeneity remains a challenging problem. In particular, identical objects may be referred to by different URIs in different data sets. Identifying such representations of the same object is called object reconciliation. In this paper, we propose a novel approach to object reconciliation that is based on an existing semantic similarity measure for linked data. We adapt the measure to the object reconciliation problem, present exact and approximate algorithms that efficiently implement the methods, and provide a systematic experimental evaluation based on a benchmark dataset. As our main result, we show that the use of light-weight ontologies and schema information significantly improves object reconciliation in the context of linked open data.


Integer Linear Programming Mixed Integer Linear Programming Graph Match Integer Linear Programming Problem Ontology Match 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Jan Noessner
    • 1
  • Mathias Niepert
    • 1
  • Christian Meilicke
    • 1
  • Heiner Stuckenschmidt
    • 1
  1. 1.KR & KM Research GroupUniversity of MannheimMannheimGermany

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